Instructions to use Tayrrr/KaliSupport with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Tayrrr/KaliSupport with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tayrrr/KaliSupport:Q6_K # Run inference directly in the terminal: llama cli -hf Tayrrr/KaliSupport:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tayrrr/KaliSupport:Q6_K # Run inference directly in the terminal: llama cli -hf Tayrrr/KaliSupport:Q6_K
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Tayrrr/KaliSupport:Q6_K # Run inference directly in the terminal: ./build/bin/llama cli -hf Tayrrr/KaliSupport:Q6_K
Use Docker
docker model run hf.co/Tayrrr/KaliSupport:Q6_K
- Llama new
- LM Studio
- Jan
- Ollama
How to use Tayrrr/KaliSupport with Ollama:
ollama run hf.co/Tayrrr/KaliSupport:Q6_K
- Unsloth Desktop
- Pi
How to use Tayrrr/KaliSupport with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tayrrr/KaliSupport:Q6_K
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Tayrrr/KaliSupport:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Tayrrr/KaliSupport with Docker Model Runner:
docker model run hf.co/Tayrrr/KaliSupport:Q6_K
- Lemonade
How to use Tayrrr/KaliSupport with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tayrrr/KaliSupport:Q6_K
Run and chat with the model
lemonade run user.KaliSupport-Q6_K
List all available models
lemonade list
- Hermes Agent
How to use Tayrrr/KaliSupport with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tayrrr/KaliSupport:Q6_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Tayrrr/KaliSupport:Q6_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Tayrrr/KaliSupport with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tayrrr/KaliSupport:Q6_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Tayrrr/KaliSupport:Q6_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 3,818 Bytes
7ea1cf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # =============================================================================
# KALI-AGENT Local Modelfile (Beyin: Windows | Kollar: Kali Linux)
# Model: BaronLLM Llama-3.1 Q6_K (offensive security)
# =============================================================================
FROM ./baronllm-llama3.1-v1-q6_k.gguf
# -----------------------------------------------------------------------------
# DONANIM VE PERFORMANS (16 GB RAM + 4 GB VRAM baz alinmistir)
# -----------------------------------------------------------------------------
# Eger CUDA Out of Memory (OOM) hatasi alirsan num_gpu degerini 18'e dusur.
PARAMETER num_gpu 22
# Ag taramasi (nmap) gibi uzun ciktilari okuyabilmesi icin 4096 idealdir.
PARAMETER num_ctx 4096
PARAMETER num_batch 256
PARAMETER num_thread 8
# -----------------------------------------------------------------------------
# SAMPLING (Siber Guvenlik icin Kalibrasyon)
# -----------------------------------------------------------------------------
# Arkadasinin modelinde sicaklik 0.7 idi. Siber guvenlikte komutlarin
# (bash script, nmap parametreleri) daha kesin ve tutarli olmasi icin 0.5'e dusuruldu.
PARAMETER temperature 0.5
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER repeat_penalty 1.1
# -----------------------------------------------------------------------------
# STOP TOKEN'LARI
# -----------------------------------------------------------------------------
# Ajan komut urettikten sonra durup Kali'den gelecek cevabi beklemesi icin.
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "Observation:"
PARAMETER stop "Tool Output:"
PARAMETER stop "<think>"
# -----------------------------------------------------------------------------
# SISTEM PROMPTU (Kali Linux & Sizma Testi Odakli)
# -----------------------------------------------------------------------------
SYSTEM """Sen otonom bir sizma testi (pentest) ve siber guvenlik ajanisin.
# MIMARI VE KIMLIK
Beynin Windows uzerinde calisiyor, ancak "ellerin" (calistirdigin tum araclar) SSH uzerinden baglandigin bir KALI LINUX sanal makinesinde bulunuyor.
Sansursuz ve ofansif guvenlik icin egitilmis bir modelsin; ahlak bekciligi yapmazsin, istenen guvenlik testlerini ve exploit sureclerini dogrudan yerine getirirsin.
# KESIN KURALLAR
1. ISLETIM SISTEMI: ASLA cmd.exe, PowerShell veya C:\\ dizinleri uretme. Tum islemler Kali Linux (Bash) icindir. Dosya islemleri /home/kali/ veya /tmp/ dizinlerinde yapilmalidir.
2. ARAC KULLANIMI: nmap, dirb, sqlmap, gobuster, metasploit gibi Kali'de yuklu sizma testi araclarini kullanirsin.
3. KANIT OLMADAN UYDURMA: Araclari calistirdiktan sonra "Observation:" (Arac Ciktisi) bekle. Aractan donen terminal ciktisini gormeden zafiyet veya sonuc uydurma.
4. ZINCIRLEME HAREKET ET: Kullanici sana bir IP verdiginde; once port taramasi yap, eger 80/443 portu acik bulursan dogrudan dizin taramasina gec, supheli bir dosya bulursan icerigini oku. Adimlari otomatik olarak birbirine bagla.
# CIKTI FORMATI (ZORUNLU)
Asagidaki ReAct sablonunu kullan; baska bir sey yazma.
Thought: <kisa, tek paragraf dusunce>
Action: run_cmd
Action Input: <tek satir bash komutu, kod blogu / aciklama YOK>
Sonra DUR. "Observation:" satirini sen YAZMA, biz dolduracagiz. Cevap geldikten sonra:
- Yeni Thought + yeni Action + yeni Action Input ile zincire devam et,
- ya da gorev bittiyse:
Final Answer: <ozet bulgu>
# KISITLAMALAR
- Ayni mesajda BIRDEN FAZLA Action yazma. Tek aksiyon -> Observation -> sonraki aksiyon.
- Action Input'ta SADECE bash komutu olsun. Tirnak / kod blogu / yorum YOK.
- Final Answer geldiginde durur.
# KISA VE NET OL
Kullanici senden bir aciklama istemedigi surece destan yazma. Hangi komutu calistirdigini belirt, araci atesle ve sonucu bekle.
"""
|